Improved binary dragonfly optimization algorithm and wavelet packet based non-linear features for infant cry classification

Hariharan, M.; Sindhu, Ritu; Vijean, Vikneswaran; Yazid, Haniza; Nadarajaw, Thiyagar; Yaacob, Sazali; Polat, Kemal

doi:10.1016/j.cmpb.2017.11.021

Cited by 68 publications

(23 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Before the recognition and classification of sleep stages, some feature dimensionality reduction or feature selection algorithms may be used to process the data to handle the large dimension of the extracted feature dataset, which affects the classification effect of the classifier [ 53 ]. For example, PCA and independent component analysis (ICA) can only measure the linear relationship between variables and may ignore important information [ 54 ].…”

Section: Discussionmentioning

confidence: 99%

“…A six-dimensional feature matrix with a combination of 24 × 40 × 4 × 4 × 5 × 18 was obtained by 24 subjects and we adjusted the dimensions to a 15,360 × 90 feature matrix. The number and dimensions of these features are large and the feature dataset has too many dimensions or redundant features, which not only poses challenges to the classifier design and training but also worsens the classification effect and considerably increases computational complexity because of the possible “dimension disaster” [ 53 ]. Therefore, it is necessary to select the feature matrix.…”

Section: Experimental Design and Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Microwave Treatment for Sleep Disorders and Classification of Sleep Stages Using Multi-Scale Entropy

Geng

Yang

Cai

et al. 2020

Entropy

View full text Add to dashboard Cite

The aim of this study was to develop an integrated system of non-contact sleep stage detection and sleep disorder treatment for health monitoring. Hence, a method of brain activity detection based on microwave scattering technology instead of scalp electroencephalogram was developed to evaluate the sleep stage. First, microwaves at a specific frequency were used to penetrate the functional sites of the brain in patients with sleep disorders to change the firing frequency of the activated areas of the brain and analyze and evaluate statistically the effects on sleep improvement. Then, a wavelet packet algorithm was used to decompose the microwave transmission signal, the refined composite multiscale sample entropy, the refined composite multiscale fluctuation-based dispersion entropy and multivariate multiscale weighted permutation entropy were obtained as features from the wavelet packet coefficient. Finally, the mutual information-principal component analysis feature selection method was used to optimize the feature set and random forest was used to classify and evaluate the sleep stage. The results show that after four times of microwave modulation treatment, sleep efficiency improved continuously, the overall maintenance was above 80%, and the insomnia rate was reduced gradually. The overall classification accuracy of the four sleep stages was 86.4%. The results indicate that the microwaves with a certain frequency can treat sleep disorders and detect abnormal brain activity. Therefore, the microwave scattering method is of great significance in the development of a new brain disease treatment, diagnosis and clinical application system.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Experimental Design and Methodsmentioning

confidence: 99%

A Novel Microwave Treatment for Sleep Disorders and Classification of Sleep Stages Using Multi-Scale Entropy

Geng

Yang

Cai

et al. 2020

Entropy

View full text Add to dashboard Cite

show abstract

“…Hariharan et al [47] proposed a combination technique of wavelet packet-based features and the improved version of binary dragonfly optimization (IBDFO) algorithm-based feature selection that was used for classifying various signal types of infant cry. Cry signals were obtained from two different databases.…”

Section: Applications Of Damentioning

confidence: 99%

Dragonfly Algorithm and Its Applications in Applied Science Survey

Rahman

Rashid

2019

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

One of the most recently developed heuristic optimization algorithms is dragonfly by Mirjalili. Dragonfly algorithm has shown its ability to optimizing different real-world problems. It has three variants. In this work, an overview of the algorithm and its variants is presented. Moreover, the hybridization versions of the algorithm are discussed. Furthermore, the results of the applications that utilized the dragonfly algorithm in applied science are offered in the following area: machine learning, image processing, wireless, and networking. It is then compared with some other metaheuristic algorithms. In addition, the algorithm is tested on the CEC-C06 2019 benchmark functions. The results prove that the algorithm has great exploration ability and its convergence rate is better than the other algorithms in the literature, such as PSO and GA. In general, in this survey, the strong and weak points of the algorithm are discussed. Furthermore, some future works that will help in improving the algorithm's weak points are recommended. This study is conducted with the hope of offering beneficial information about dragonfly algorithm to the researchers who want to study the algorithm.

show abstract

“…Tawhid et al [58] combine the BDA and enhanced PSO to propose a hybrid BDA-enhanced PSO (HBDESPO) algorithm for feature selections. In reference [59], a combination of wavelet packet-based features and improved binary dragonfly optimization-based feature selection method is proposed to classify different types of infant cry signals. Moreover, Sayed et al [60] propose a chaotic DA (CDA) where the chaotic maps are embedded with the searching iterations of the algorithm for feature selections.…”

Section: Related Workmentioning

confidence: 99%

IBDA: Improved Binary Dragonfly Algorithm With Evolutionary Population Dynamics and Adaptive Crossover for Feature Selection

Kang

Sun

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Feature selection is an effective method to eliminate irrelevant, redundant and noisy features, which improves the performance of classification and reduces the computational burden in machine learning. In this paper, an improved binary dragonfly algorithm (IBDA) which extends from the conventional dragonfly algorithm (DA) is proposed as a search strategy to design a wrapper-based feature selection method. First, a novel evolutionary population dynamics (EPD) strategy is introduced in IBDA to enhance the exploitation ability while ensuring population diversity of the algorithm. Second, IBDA proposes a novel crossover operator which establishes connections between the crossover rates and iterations so that making the algorithm can adjust the crossover rates of solutions dynamically, thereby balancing the exploitation and exploration of the algorithm. Finally, a binary mechanism is proposed to make the algorithm suitable for the binary feature selection problems. Simulations are conducted on 27 classical datasets from the UC Irvine Machine Learning Repository, and the results demonstrate that the proposed IBDA has better performance than some other comparison algorithms. Moreover, the effectiveness and performance of the proposed improved factors are evaluated by tests.

show abstract

Improved binary dragonfly optimization algorithm and wavelet packet based non-linear features for infant cry classification

Cited by 68 publications

References 40 publications

A Novel Microwave Treatment for Sleep Disorders and Classification of Sleep Stages Using Multi-Scale Entropy

A Novel Microwave Treatment for Sleep Disorders and Classification of Sleep Stages Using Multi-Scale Entropy

Dragonfly Algorithm and Its Applications in Applied Science Survey

IBDA: Improved Binary Dragonfly Algorithm With Evolutionary Population Dynamics and Adaptive Crossover for Feature Selection

Contact Info

Product

Resources

About